Cell-based Simulations of Biased Epithelial Lung Growth

Abstract

During morphogenesis, epithelial tubes elongate. In case of the mammalian lung, biased elongation has been linked to a bias in cell shape and cell division, but it has remained unclear whether a bias in cell shape along the axis of outgrowth is sufficient for biased outgrowth and how it arises. Here, we use our 2D cell-based tissue simulation software LBIBCell to investigate the conditions for biased epithelial outgrowth. We show that the observed bias in cell shape and cell division can result in the observed bias in outgrowth only in case of strong cortical tension, and comparison to biological data suggests that the cortical tension in epithelia is likely sufficient. We explore mechanisms that may result in the observed bias in cell division and cell shapes. To this end, we test the possibility that the surrounding tissue or extracellular matrix acts as a mechanical constraint that biases growth in longitudinal direction. While external compressive forces can result in the observed bias in outgrowth, we find that they do not result in the observed bias in cell shapes. We conclude that other mechanisms must exist that generate the bias in lung epithelial outgrowth.

The Biomechanical Basis of Biased Epithelial Tube Elongation in Lung and Kidney Development

“…However, it is unclear how such an outer wall-like constricting force would arise even in the absence of mesenchyme. Moreover, we have shown before that a constricting force that results in the observed biased epithelial outgrowth in a cell-based model is insufficient to generate the observed bias in cell shape and cell division (Stopka et al, 2019). Consequently, the mechanical constraints explored here are unlikely to drive the biased elongating outgrowth of embryonic lung tubes.…”

“…The measured flow velocity is sufficiently high that the resulting shear stress can be sensed by epithelial cells with their primary cilium (Weinbaum et al, 2011). We evaluate the impact of shear stress in a cell-based tissue model, and find that shear stress, unlike constricting forces (Stopka et al, 2019), can explain both the observed biased tube elongation and the observed bias in cell division. Shear stress may thus be a more general driver of biased tube elongation beyond its established role in angiogenesis (Davies, 2009;Galbraith et al, 1998;Galie et al, 2014).…”

The control of lung branching morphogenesis

“…However, lung branches are thinner rather than wider when inactivation of Myocardin prevents the formation of airway smooth muscles (Young et al, 2020), and biased lung tube elongation is strongest before airway smooth muscles become detectable at E11.5 (Hines et al, 2013;Tang et al, 2011), and is observed also in the absence of mesenchyme . Furthermore, cell-based simulations show that constricting forces that generate the observed bias in outgrowth, result in a much lower cell shape bias than what is observed in the embryonic lung epithelium (Stopka et al, 2019). Constricting forces are therefore unlikely to drive the elongating outgrowth of lung tubes.…”

“…In the lung, the mitotic spindle becomes biased once the cell aspect ratio at interphase is higher than 1.53 (Tang et al, 2018). Computational modelling shows that such a bias in cell shape and cell division can, in principle, result in biased outgrowth Stopka et al, 2019;Tang et al, 2011). So, what leads to this bias in cell shape?…”

Toward Engineering Biosystems With Emergent Collective Functions

“…In particular, multi-agent models are able to capture the role of cellular heterogeneity, proliferation and morphology, mechanical and environmental cues, movement of cells as well as the integration of multiple processes at diverse scales and the feedback between these ( Montes-Olivas et al, 2019 ). Such models have helped deepen our understanding of early mammalian embryogenesis ( Godwin et al, 2017 ), as well as the formation of vascular networks ( Perfahl et al, 2017 ) and other complex structures and organs, including the skin, lung ( Stopka et al, 2019 ), kidney ( Lambert et al, 2018 ), and brain ( Caffrey et al, 2014 ).…”